Articles | Volume 18, issue 1
https://doi.org/10.5194/nhess-18-185-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-18-185-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Jan 2018
Research article |
| 16 Jan 2018
| 16 Jan 2018
Satellite-based emergency mapping using optical imagery: experience and reflections from the 2015 Nepal earthquakes
Jack G. Williams
CORRESPONDING AUTHOR
Institute of Hazard, Risk and Resilience and Department of Geography, Durham University, Lower Mountjoy, South Road, Durham, DH1 3LE, UK
Nick J. Rosser
Institute of Hazard, Risk and Resilience and Department of Geography, Durham University, Lower Mountjoy, South Road, Durham, DH1 3LE, UK
Mark E. Kincey
Institute of Hazard, Risk and Resilience and Department of Geography, Durham University, Lower Mountjoy, South Road, Durham, DH1 3LE, UK
Jessica Benjamin
Institute of Hazard, Risk and Resilience and Department of Geography, Durham University, Lower Mountjoy, South Road, Durham, DH1 3LE, UK
Katie J. Oven
Institute of Hazard, Risk and Resilience and Department of Geography, Durham University, Lower Mountjoy, South Road, Durham, DH1 3LE, UK
Alexander L. Densmore
Institute of Hazard, Risk and Resilience and Department of Geography, Durham University, Lower Mountjoy, South Road, Durham, DH1 3LE, UK
David G. Milledge
Institute of Hazard, Risk and Resilience and Department of Geography, Durham University, Lower Mountjoy, South Road, Durham, DH1 3LE, UK
Tom R. Robinson
Institute of Hazard, Risk and Resilience and Department of Geography, Durham University, Lower Mountjoy, South Road, Durham, DH1 3LE, UK
Colm A. Jordan
British Geological Survey, Natural Environment Research Council, Environmental Science Centre, Keyworth, Nottingham, NG12 5GG, UK
Tom A. Dijkstra
British Geological Survey, Natural Environment Research Council, Environmental Science Centre, Keyworth, Nottingham, NG12 5GG, UK
School of Architecture, Building and Civil Engineering, Loughborough University, Ashby Road, Loughborough, LE11 3TU, UK
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Veit Ulrich, Jack G. Williams, Vivien Zahs, Katharina Anders, Stefan Hecht, and Bernhard Höfle
Earth Surf. Dynam., 9, 19–28, https://doi.org/10.5194/esurf-9-19-2021, https://doi.org/10.5194/esurf-9-19-2021, 2021
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In this work, we use 3D point clouds to detect topographic changes across the surface of a rock glacier. These changes are presented as the relative contribution of surface change during a 3-week period to the annual surface change. By comparing these different time periods and looking at change in different directions, we provide estimates showing that different directions of surface change are dominant at different times of the year. This demonstrates the benefit of frequent monitoring.
Jack G. Williams, Nick J. Rosser, Richard J. Hardy, Matthew J. Brain, and Ashraf A. Afana
Earth Surf. Dynam., 6, 101–119, https://doi.org/10.5194/esurf-6-101-2018, https://doi.org/10.5194/esurf-6-101-2018, 2018
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We present a method to analyse surface change using 3-D data collected at hourly intervals. This is applied to 9000 surveys of a failing rock slope, acquired over 10 months. A higher proportion and frequency of small rockfall is observed than in less-frequent (e.g. monthly) monitoring. However, quantifying longer-term erosion rates may be more suited to less-frequent data collection, which contains lower accumulative errors due to the number of surveys and the lower proportion of small events.
Tom R. Robinson, Nicholas J. Rosser, Alexander L. Densmore, Jack G. Williams, Mark E. Kincey, Jessica Benjamin, and Heather J. A. Bell
Nat. Hazards Earth Syst. Sci., 17, 1521–1540, https://doi.org/10.5194/nhess-17-1521-2017, https://doi.org/10.5194/nhess-17-1521-2017, 2017
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Current methods to identify landslides after an earthquake are too slow to effectively inform emergency response operations. This study presents an empirical approach for modelling the spatial pattern and landslide density within hours to days of the earthquake. The approach uses small initial samples of landslides to identify locations where as yet unidentified landslides may have occurred. The model requires just 200 initial landslides, provided they have sufficiently wide spatial coverage.
Katy Burrows, David G. Milledge, and Maria Francesca Ferrario
EGUsphere, https://doi.org/10.5194/egusphere-2024-3264, https://doi.org/10.5194/egusphere-2024-3264, 2024
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In 2018, 6 moderate-large earthquakes occurred in Lombok, Indonesia over a 3-week period, triggering landslides across the island. Their locations were previously mapped with optical satellite images, but information on which earthquake triggered which landslide was limited. Here we use Sentinel-1 satellite images to determine when during the earthquake sequence many of the landslides failed and so build a more complete picture of how landslide activity evolved through time.
Alex Dunant, Tom R. Robinson, Alexander Logan Densmore, Nick J. Rosser, Ragindra Man Rajbhandari, Mark Kincey, Sihan Li, Prem Raj Awasthi, Max Van Wyk de Vries, Ramesh Guragain, Erin Harvey, and Simon Dadson
EGUsphere, https://doi.org/10.5194/egusphere-2024-1374, https://doi.org/10.5194/egusphere-2024-1374, 2024
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Our study introduces a new method using hypergraph theory to assess risks from interconnected natural hazards. Traditional models often overlook how these hazards can interact and worsen each other's effects. By applying our method to the 2015 Nepal earthquake, we successfully demonstrated its ability to predict broad damage patterns, despite slightly overestimating impacts. Being able to anticipate the effects of complex, interconnected hazards is critical for disaster preparedness.
Maximillian Van Wyk de Vries, Sihan Li, Katherine Arrell, Jeevan Baniya, Dipak Basnet, Gopi K. Basyal, Nyima Dorjee Bhotia, Alexander L. Densmore, Tek Bahadur Dong, Alexandre Dunant, Erin L. Harvey, Ganesh K. Jimee, Mark E. Kincey, Katie Oven, Sarmila Paudyal, Dammar Singh Pujara, Anuradha Puri, Ram Shrestha, Nick J. Rosser, and Simon J. Dadson
EGUsphere, https://doi.org/10.5194/egusphere-2024-397, https://doi.org/10.5194/egusphere-2024-397, 2024
Preprint archived
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This study focuses on understanding soil moisture, a key factor for evaluating hillslope stability and landsliding. In Nepal, where landslides are common, we used a computer model to better understand how rapidly soil dries out after the monsoon season. We calibrated the model using field data and found that, by adjusting soil properties, we could predict moisture levels more accurately. This helps understand where landslides might occur, even where direct measurements are not possible.
Maximillian Van Wyk de Vries, Alexandre Dunant, Amy L. Johnson, Erin L. Harvey, Sihan Li, Katherine Arrell, Jeevan Baniya, Dipak Basnet, Gopi K. Basyal, Nyima Dorjee Bhotia, Simon J. Dadson, Alexander L. Densmore, Tek Bahadur Dong, Mark E. Kincey, Katie Oven, Anuradha Puri, and Nick J. Rosser
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-40, https://doi.org/10.5194/nhess-2024-40, 2024
Revised manuscript under review for NHESS
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Mapping exposure to landslides is necessary to mitigate risk and reduce vulnerability. In this study, we show that there is a poor correlation between building damage and deaths from landslides- such that the deadliest landslides do not always destroy the most buildings and vice versa. This has important implications for our management on landslide risk.
David G. Milledge, Dino G. Bellugi, Jack Watt, and Alexander L. Densmore
Nat. Hazards Earth Syst. Sci., 22, 481–508, https://doi.org/10.5194/nhess-22-481-2022, https://doi.org/10.5194/nhess-22-481-2022, 2022
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Earthquakes can trigger thousands of landslides, causing severe and widespread damage. Efforts to understand what controls these landslides rely heavily on costly and time-consuming manual mapping from satellite imagery. We developed a new method that automatically detects landslides triggered by earthquakes using thousands of free satellite images. We found that in the majority of cases, it was as skilful at identifying the locations of landslides as the manual maps that we tested it against.
Katy Burrows, David Milledge, Richard J. Walters, and Dino Bellugi
Nat. Hazards Earth Syst. Sci., 21, 2993–3014, https://doi.org/10.5194/nhess-21-2993-2021, https://doi.org/10.5194/nhess-21-2993-2021, 2021
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When cloud cover obscures optical satellite imagery, there are two options remaining for generating information on earthquake-triggered landslide locations: (1) models which predict landslide locations based on, e.g., slope and ground shaking data and (2) satellite radar data, which penetrates cloud cover and is sensitive to landslides. Here we show that the two approaches can be combined to give a more consistent and more accurate model of landslide locations after an earthquake.
Thomas Croissant, Robert G. Hilton, Gen K. Li, Jamie Howarth, Jin Wang, Erin L. Harvey, Philippe Steer, and Alexander L. Densmore
Earth Surf. Dynam., 9, 823–844, https://doi.org/10.5194/esurf-9-823-2021, https://doi.org/10.5194/esurf-9-823-2021, 2021
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In mountain ranges, earthquake-derived landslides mobilize large amounts of organic carbon (OC) by eroding soil from hillslopes. We propose a model to explore the role of different parameters in the post-seismic redistribution of soil OC controlled by fluvial export and heterotrophic respiration. Applied to the Southern Alps, our results suggest that efficient OC fluvial export during the first decade after an earthquake promotes carbon sequestration.
Veit Ulrich, Jack G. Williams, Vivien Zahs, Katharina Anders, Stefan Hecht, and Bernhard Höfle
Earth Surf. Dynam., 9, 19–28, https://doi.org/10.5194/esurf-9-19-2021, https://doi.org/10.5194/esurf-9-19-2021, 2021
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In this work, we use 3D point clouds to detect topographic changes across the surface of a rock glacier. These changes are presented as the relative contribution of surface change during a 3-week period to the annual surface change. By comparing these different time periods and looking at change in different directions, we provide estimates showing that different directions of surface change are dominant at different times of the year. This demonstrates the benefit of frequent monitoring.
Katy Burrows, Richard J. Walters, David Milledge, and Alexander L. Densmore
Nat. Hazards Earth Syst. Sci., 20, 3197–3214, https://doi.org/10.5194/nhess-20-3197-2020, https://doi.org/10.5194/nhess-20-3197-2020, 2020
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Satellite radar could provide information on landslide locations within days of an earthquake or rainfall event anywhere on Earth, but until now there has been a lack of systematic testing of possible radar methods, and most methods have been demonstrated using a single case study event and data from a single satellite sensor. Here we test five methods on four events, demonstrating their wide applicability and making recommendations on when different methods should be applied in the future.
David G. Milledge, Alexander L. Densmore, Dino Bellugi, Nick J. Rosser, Jack Watt, Gen Li, and Katie J. Oven
Nat. Hazards Earth Syst. Sci., 19, 837–856, https://doi.org/10.5194/nhess-19-837-2019, https://doi.org/10.5194/nhess-19-837-2019, 2019
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Mitigating landslide risk requires information on landslide hazards on a suitable scale to inform decisions. We develop simple rules to identify landslide hazards and the probability of being hit by a landslide, then test their performance using six existing landslide inventories from recent earthquakes. We find that the best rules are "minimize your maximum look angle to the skyline" and "avoid steep (> 10˚) channels with many steep (> 40˚) areas that are upslope".
Jack G. Williams, Nick J. Rosser, Richard J. Hardy, Matthew J. Brain, and Ashraf A. Afana
Earth Surf. Dynam., 6, 101–119, https://doi.org/10.5194/esurf-6-101-2018, https://doi.org/10.5194/esurf-6-101-2018, 2018
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We present a method to analyse surface change using 3-D data collected at hourly intervals. This is applied to 9000 surveys of a failing rock slope, acquired over 10 months. A higher proportion and frequency of small rockfall is observed than in less-frequent (e.g. monthly) monitoring. However, quantifying longer-term erosion rates may be more suited to less-frequent data collection, which contains lower accumulative errors due to the number of surveys and the lower proportion of small events.
Tom R. Robinson, Nicholas J. Rosser, Alexander L. Densmore, Jack G. Williams, Mark E. Kincey, Jessica Benjamin, and Heather J. A. Bell
Nat. Hazards Earth Syst. Sci., 17, 1521–1540, https://doi.org/10.5194/nhess-17-1521-2017, https://doi.org/10.5194/nhess-17-1521-2017, 2017
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Current methods to identify landslides after an earthquake are too slow to effectively inform emergency response operations. This study presents an empirical approach for modelling the spatial pattern and landslide density within hours to days of the earthquake. The approach uses small initial samples of landslides to identify locations where as yet unidentified landslides may have occurred. The model requires just 200 initial landslides, provided they have sufficiently wide spatial coverage.
Robert N. Parker, Nicholas J. Rosser, and Tristram C. Hales
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2017-193, https://doi.org/10.5194/nhess-2017-193, 2017
Revised manuscript has not been submitted
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In mountainous regions, large earthquakes often trigger widespread and destructive landslides. Understanding and predicting where these landslides occur is important for assessing hazards, as well as investigating their impact on the physical landscape. Based on correlations between landslides and different landscape and earthquake characteristics in nine past earthquakes, we developed a generalised algorithm for predicting and mapping the probability of earthquake-triggered landslides.
R. N. Parker, G. T. Hancox, D. N. Petley, C. I. Massey, A. L. Densmore, and N. J. Rosser
Earth Surf. Dynam., 3, 501–525, https://doi.org/10.5194/esurf-3-501-2015, https://doi.org/10.5194/esurf-3-501-2015, 2015
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Large earthquakes commonly trigger widespread and destructive landsliding. This paper tests the hypothesis that spatial distributions of earthquake-induced landslides are determined by both the conditions at the time of the triggering earthquake and the legacy of past events. Our findings emphasise that a lack of understanding of the legacy of damage in hillslopes potentially represents an important source of uncertainty when assessing regional landslide susceptibility.
C. Hahn, V. Prasuhn, C. Stamm, D. G. Milledge, and R. Schulin
Hydrol. Earth Syst. Sci., 18, 2975–2991, https://doi.org/10.5194/hess-18-2975-2014, https://doi.org/10.5194/hess-18-2975-2014, 2014
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Assessing landslide damming susceptibility in Central Asia
Invited Perspectives: Integrating hydrologic information into the next generation of landslide early warning systems
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Comparison of conditioning factors classification criteria in large scale statistically based landslide susceptibility models
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A new analytical method for stability analysis of rock blocks with basal erosion in sub-horizontal strata by considering the eccentricity effect
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Landslide initiation thresholds in data-sparse regions: application to landslide early warning criteria in Sitka, Alaska, USA
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The concept of event-size-dependent exhaustion and its application to paraglacial rockslides
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Li Wei, Kaiheng Hu, Shuang Liu, Lan Ning, Xiaopeng Zhang, Qiyuan Zhang, and Md. Abdur Rahim
Nat. Hazards Earth Syst. Sci., 24, 4179–4197, https://doi.org/10.5194/nhess-24-4179-2024, https://doi.org/10.5194/nhess-24-4179-2024, 2024
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The damage patterns of the buildings were classified into three types: (I) buried by primary debris flow, (II) inundated by secondary dam-burst flood, and (III) sequentially buried by debris flow and inundated by dam-burst flood. The threshold of the impact pressures in Zones (II) and (III) where vulnerability is equal to 1 is 84 kPa and 116 kPa, respectively. Heavy damage occurs at an impact pressure greater than 50 kPa, while slight damage occurs below 30 kPa.
Bo Peng and Xueling Wu
Nat. Hazards Earth Syst. Sci., 24, 3991–4013, https://doi.org/10.5194/nhess-24-3991-2024, https://doi.org/10.5194/nhess-24-3991-2024, 2024
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Our research enhances landslide prevention using advanced machine learning to forecast heavy-rainfall-triggered landslides. By analyzing regions and employing various models, we identified optimal ways to predict high-risk rainfall events. Integrating multiple factors and models, including a neural network, significantly improves landslide predictions. Real data validation confirms our approach's reliability, aiding communities in mitigating landslide impacts and safeguarding lives and property.
Andrea Manconi, Yves Bühler, Andreas Stoffel, Johan Gaume, Qiaoping Zhang, and Valentyn Tolpekin
Nat. Hazards Earth Syst. Sci., 24, 3833–3839, https://doi.org/10.5194/nhess-24-3833-2024, https://doi.org/10.5194/nhess-24-3833-2024, 2024
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Our research reveals the power of high-resolution satellite synthetic-aperture radar (SAR) imagery for slope deformation monitoring. Using ICEYE data over the Brienz/Brinzauls instability, we measured surface velocity and mapped the landslide event with unprecedented precision. This underscores the potential of satellite SAR for timely hazard assessment in remote regions and aiding disaster mitigation efforts effectively.
Oliver Korup, Lisa V. Luna, and Joaquin V. Ferrer
Nat. Hazards Earth Syst. Sci., 24, 3815–3832, https://doi.org/10.5194/nhess-24-3815-2024, https://doi.org/10.5194/nhess-24-3815-2024, 2024
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Catalogues of mapped landslides are useful for learning and forecasting how frequently they occur in relation to their size. Yet, rare and large landslides remain mostly uncertain in statistical summaries of these catalogues. We propose a single, consistent method of comparing across different data sources and find that landslide statistics disclose more about subjective mapping choices than trigger types or environmental settings.
Rachael Lau, Carolina Seguí, Tyler Waterman, Nathaniel Chaney, and Manolis Veveakis
Nat. Hazards Earth Syst. Sci., 24, 3651–3661, https://doi.org/10.5194/nhess-24-3651-2024, https://doi.org/10.5194/nhess-24-3651-2024, 2024
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This work examines the use of interferometric synthetic-aperture radar (InSAR) alongside in situ borehole measurements to assess the stability of deep-seated landslides for the case study of El Forn (Andorra). Comparing InSAR with borehole data suggests a key trade-off between accuracy and precision for various InSAR resolutions. Spatial interpolation with InSAR informed how many remote observations are necessary to lower error in a remote sensing re-creation of ground motion over the landslide.
Zhen Lei Wei, Yue Quan Shang, Qiu Hua Liang, and Xi Lin Xia
Nat. Hazards Earth Syst. Sci., 24, 3357–3379, https://doi.org/10.5194/nhess-24-3357-2024, https://doi.org/10.5194/nhess-24-3357-2024, 2024
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The initiation of debris flows is significantly influenced by rainfall-induced hydrological processes. We propose a novel framework based on an integrated hydrological and hydrodynamic model and aimed at estimating intensity–duration (ID) rainfall thresholds responsible for triggering debris flows. In comparison to traditional statistical approaches, this physically based framework is particularly suitable for application in ungauged catchments where historical debris flow data are scarce.
Jürgen Mey, Ravi Kumar Guntu, Alexander Plakias, Igo Silva de Almeida, and Wolfgang Schwanghart
Nat. Hazards Earth Syst. Sci., 24, 3207–3223, https://doi.org/10.5194/nhess-24-3207-2024, https://doi.org/10.5194/nhess-24-3207-2024, 2024
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The Himalayan road network links remote areas, but fragile terrain and poor construction lead to frequent landslides. This study on the NH-7 in India's Uttarakhand region analyzed 300 landslides after heavy rainfall in 2022 . Factors like slope, rainfall, rock type and road work influence landslides. The study's model predicts landslide locations for better road maintenance planning, highlighting the risk from climate change and increased road use.
Chenchen Qiu and Xueyu Geng
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-156, https://doi.org/10.5194/nhess-2024-156, 2024
Revised manuscript accepted for NHESS
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We proposed an interated method with the combination of a physical vulnerability matric and a machine learning model to estimate the potential physical damage and associated economic loss caused by future debris flows based on the collected historical data on the Qinghai-Tibet Plateau regions.
Fabiola Banfi, Emanuele Bevacqua, Pauline Rivoire, Sérgio C. Oliveira, Joaquim G. Pinto, Alexandre M. Ramos, and Carlo De Michele
Nat. Hazards Earth Syst. Sci., 24, 2689–2704, https://doi.org/10.5194/nhess-24-2689-2024, https://doi.org/10.5194/nhess-24-2689-2024, 2024
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Landslides are complex phenomena causing important impacts in vulnerable areas, and they are often triggered by rainfall. Here, we develop a new approach that uses information on the temporal clustering of rainfall, i.e. multiple events close in time, to detect landslide events and compare it with the use of classical empirical rainfall thresholds, considering as a case study the region of Lisbon, Portugal. The results could help to improve the prediction of rainfall-triggered landslides.
Jianqi Zhuang, Jianbing Peng, Chenhui Du, Yi Zhu, and Jiaxu Kong
Nat. Hazards Earth Syst. Sci., 24, 2615–2631, https://doi.org/10.5194/nhess-24-2615-2024, https://doi.org/10.5194/nhess-24-2615-2024, 2024
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The Revised Infinite Slope Model (RISM) is proposed using the equal differential unit method and correcting the deficiency of the safety factor increasing with the slope increasing when the slope is larger than 40°, as calculated using the Taylor slope infinite model. The intensity–duration (I–D) prediction curve of the rainfall-induced shallow loess landslides with different slopes was constructed and can be used in forecasting regional shallow loess landslides.
Alexander B. Prescott, Luke A. McGuire, Kwang-Sung Jun, Katherine R. Barnhart, and Nina S. Oakley
Nat. Hazards Earth Syst. Sci., 24, 2359–2374, https://doi.org/10.5194/nhess-24-2359-2024, https://doi.org/10.5194/nhess-24-2359-2024, 2024
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Fire can dramatically increase the risk of debris flows to downstream communities with little warning, but hazard assessments have not traditionally included estimates of inundation. We unify models developed by the scientific community to create probabilistic estimates of inundation area in response to rainfall at forecast lead times (≥ 24 h) needed for decision-making. This work takes an initial step toward a near-real-time postfire debris-flow inundation hazard assessment product.
Francis K. Rengers, Samuel Bower, Andrew Knapp, Jason W. Kean, Danielle W. vonLembke, Matthew A. Thomas, Jaime Kostelnik, Katherine R. Barnhart, Matthew Bethel, Joseph E. Gartner, Madeline Hille, Dennis M. Staley, Justin K. Anderson, Elizabeth K. Roberts, Stephen B. DeLong, Belize Lane, Paxton Ridgway, and Brendan P. Murphy
Nat. Hazards Earth Syst. Sci., 24, 2093–2114, https://doi.org/10.5194/nhess-24-2093-2024, https://doi.org/10.5194/nhess-24-2093-2024, 2024
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Every year the U.S. Geological Survey produces 50–100 postfire debris-flow hazard assessments using models for debris-flow likelihood and volume. To refine these models they must be tested with datasets that clearly document rainfall, debris-flow response, and debris-flow volume. These datasets are difficult to obtain, but this study developed and analyzed a postfire dataset with more than 100 postfire storm responses over a 2-year period. We also proposed ways to improve these models.
Jui-Sheng Chou, Hoang-Minh Nguyen, Huy-Phuong Phan, and Kuo-Lung Wang
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-86, https://doi.org/10.5194/nhess-2024-86, 2024
Revised manuscript accepted for NHESS
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This study enhances landslide prediction using advanced machine learning, including new algorithms inspired by historical explorations. The research accurately forecasts landslide movements by analyzing eight years of data from Taiwan's Lushan Mountain, improving early warnings and potentially saving lives and infrastructure. This integration marks a significant advancement in environmental risk management.
Praveen Kumar, Priyanka Priyanka, Kala Venkata Uday, and Varun Dutt
Nat. Hazards Earth Syst. Sci., 24, 1913–1928, https://doi.org/10.5194/nhess-24-1913-2024, https://doi.org/10.5194/nhess-24-1913-2024, 2024
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Our study focuses on predicting soil movement to mitigate landslide risks. We develop machine learning models with oversampling techniques to address the class imbalance in monitoring data. The dynamic ensemble model with K-means SMOTE (synthetic minority oversampling technique) achieves high precision, high recall, and a high F1 score. Our findings highlight the potential of these models with oversampling techniques to improve soil movement predictions in landslide-prone areas.
Kristian Svennevig, Julian Koch, Marie Keiding, and Gregor Luetzenburg
Nat. Hazards Earth Syst. Sci., 24, 1897–1911, https://doi.org/10.5194/nhess-24-1897-2024, https://doi.org/10.5194/nhess-24-1897-2024, 2024
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In our study, we analysed publicly available data in order to investigate the impact of climate change on landslides in Denmark. Our research indicates that the rising groundwater table due to climate change will result in an increase in landslide activity. Previous incidents of extremely wet winters have caused damage to infrastructure and buildings due to landslides. This study is the first of its kind to exclusively rely on public data and examine landslides in Denmark.
Christoph Schaller, Luuk Dorren, Massimiliano Schwarz, Christine Moos, Arie C. Seijmonsbergen, and E. Emiel van Loon
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-76, https://doi.org/10.5194/nhess-2024-76, 2024
Revised manuscript accepted for NHESS
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We developed a machine learning-based approach to predict the potential thickness of shallow landslides to generate improved inputs for slope stability models. We selected 21 explanatory variables including metrics on terrain, geomorphology, vegetation height, and lithology and used data from two Swiss field inventories to calibrate and test the models. The best performing machine learning model consistently reduced the mean average error by least 17 % compared to previously existing models.
Jiao Wang, Zhangxing Wang, Guanhua Sun, and Hongming Luo
Nat. Hazards Earth Syst. Sci., 24, 1741–1756, https://doi.org/10.5194/nhess-24-1741-2024, https://doi.org/10.5194/nhess-24-1741-2024, 2024
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With a simplified formula linking rainfall and groundwater level, the rise of the phreatic surface within the slope can be obtained. Then, a global analysis method that considers both seepage and seismic forces is proposed to determine the safety factor of slopes subjected to the combined effect of rainfall and earthquakes. By taking a slope in the Three Gorges Reservoir area as an example, the safety evolution of the slope combined with both rainfall and earthquake is also examined.
Carlo Tacconi Stefanelli, William Frodella, Francesco Caleca, Zhanar Raimbekova, Ruslan Umaraliev, and Veronica Tofani
Nat. Hazards Earth Syst. Sci., 24, 1697–1720, https://doi.org/10.5194/nhess-24-1697-2024, https://doi.org/10.5194/nhess-24-1697-2024, 2024
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Central Asia regions are marked by active tectonics, high mountains with glaciers, and strong rainfall. These predisposing factors make large landslides a serious threat in the area and a source of possible damming scenarios, which endanger the population. To prevent this, a semi-automated geographic information system (GIS-)based mapping method, centered on a bivariate correlation of morphometric parameters, was applied to give preliminary information on damming susceptibility in Central Asia.
Benjamin B. Mirus, Thom A. Bogaard, Roberto Greco, and Manfred Stähli
EGUsphere, https://doi.org/10.5194/egusphere-2024-1219, https://doi.org/10.5194/egusphere-2024-1219, 2024
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Early warning of increased landslide potential provides situational awareness to reduce landslide-related losses from major storm events. For decades, landslide forecasts relied on rainfall data alone, but recent research points to the value of hydrologic information for improving predictions. In this article, we provide our perspectives on the value and limitations of integrating subsurface hillslope hydrologic monitoring data and mathematical modeling for more accurate landslide forecasts.
Rex L. Baum, Dianne L. Brien, Mark E. Reid, William H. Schulz, and Matthew J. Tello
Nat. Hazards Earth Syst. Sci., 24, 1579–1605, https://doi.org/10.5194/nhess-24-1579-2024, https://doi.org/10.5194/nhess-24-1579-2024, 2024
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We mapped potential for heavy rainfall to cause landslides in part of the central mountains of Puerto Rico using new tools for estimating soil depth and quasi-3D slope stability. Potential ground-failure locations correlate well with the spatial density of landslides from Hurricane Maria. The smooth boundaries of the very high and high ground-failure susceptibility zones enclose 75 % and 90 %, respectively, of observed landslides. The maps can help mitigate ground-failure hazards.
Katherine R. Barnhart, Christopher R. Miller, Francis K. Rengers, and Jason W. Kean
Nat. Hazards Earth Syst. Sci., 24, 1459–1483, https://doi.org/10.5194/nhess-24-1459-2024, https://doi.org/10.5194/nhess-24-1459-2024, 2024
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Debris flows are a type of fast-moving landslide that start from shallow landslides or during intense rain. Infrastructure located downstream of watersheds susceptible to debris flows may be damaged should a debris flow reach them. We present and evaluate an approach to forecast building damage caused by debris flows. We test three alternative models for simulating the motion of debris flows and find that only one can forecast the correct number and spatial pattern of damaged buildings.
Luke A. McGuire, Francis K. Rengers, Ann M. Youberg, Alexander N. Gorr, Olivia J. Hoch, Rebecca Beers, and Ryan Porter
Nat. Hazards Earth Syst. Sci., 24, 1357–1379, https://doi.org/10.5194/nhess-24-1357-2024, https://doi.org/10.5194/nhess-24-1357-2024, 2024
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Runoff and erosion increase after fire, leading to a greater likelihood of floods and debris flows. We monitored debris flow activity following a fire in western New Mexico, USA, and observed 16 debris flows over a <2-year monitoring period. Rainstorms with recurrence intervals of approximately 1 year were sufficient to initiate debris flows. All debris flows initiated during the first several months following the fire, indicating a rapid decrease in debris flow susceptibility over time.
Ken'ichi Koshimizu, Satoshi Ishimaru, Fumitoshi Imaizumi, and Gentaro Kawakami
Nat. Hazards Earth Syst. Sci., 24, 1287–1301, https://doi.org/10.5194/nhess-24-1287-2024, https://doi.org/10.5194/nhess-24-1287-2024, 2024
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Morphological conditions of drainage basins that classify the presence or absence of debris flow fans were analyzed in areas with different rock strength using decision tree analysis. The relief ratio is the most important morphological factor regardless of the geology. However, the thresholds of morphological parameters needed for forming debris flow fans differ depending on the geology. Decision tree analysis is an effective tool for evaluating the debris flow risk for each geology.
Jonathan P. Perkins, Nina S. Oakley, Brian D. Collins, Skye C. Corbett, and W. Paul Burgess
EGUsphere, https://doi.org/10.5194/egusphere-2024-873, https://doi.org/10.5194/egusphere-2024-873, 2024
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Landslides are a global issue that results in deaths and economic losses annually. However, it is not clear how storm severity relates to landslide severity across large regions. Here we develop a method to estimate the footprint of landslide area and compare this to meteorologic estimates of storm severity. We find that total storm strength does not clearly relate to landslide area. Rather, landslide area depends on soil wetness and smaller storm structures that can produce intense rainfall.
Daniel Bolliger, Fritz Schlunegger, and Brian W. McArdell
Nat. Hazards Earth Syst. Sci., 24, 1035–1049, https://doi.org/10.5194/nhess-24-1035-2024, https://doi.org/10.5194/nhess-24-1035-2024, 2024
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We analysed data from the Illgraben debris flow monitoring station, Switzerland, and we modelled these flows with a debris flow runout model. We found that no correlation exists between the grain size distribution, the mineralogical composition of the matrix, and the debris flow properties. The flow properties rather appear to be determined by the flow volume, from which most other parameters can be derived.
Yuntao Zhou, Xiaoyan Zhao, Guangze Zhang, Bernd Wünnemann, Jiajia Zhang, and Minghui Meng
Nat. Hazards Earth Syst. Sci., 24, 891–906, https://doi.org/10.5194/nhess-24-891-2024, https://doi.org/10.5194/nhess-24-891-2024, 2024
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We developed three rock bridge models to analyze 3D stability and deformation behaviors of the Tizicao landslide and found that the contact surface model with high strength parameters combines advantages of the intact rock mass model in simulating the deformation of slopes with rock bridges and the modeling advantage of the Jennings model. The results help in choosing a rock bridge model to simulate landslide stability and reveal the influence laws of rock bridges on the stability of landslides.
Ashok Dahal, Hakan Tanyas, Cees van Westen, Mark van der Meijde, Paul Martin Mai, Raphaël Huser, and Luigi Lombardo
Nat. Hazards Earth Syst. Sci., 24, 823–845, https://doi.org/10.5194/nhess-24-823-2024, https://doi.org/10.5194/nhess-24-823-2024, 2024
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We propose a modeling approach capable of recognizing slopes that may generate landslides, as well as how large these mass movements may be. This protocol is implemented, tested, and validated with data that change in both space and time via an Ensemble Neural Network architecture.
Li-Ru Luo, Zhi-Xiang Yu, Li-Jun Zhang, Qi Wang, Lin-Xu Liao, and Li Peng
Nat. Hazards Earth Syst. Sci., 24, 631–649, https://doi.org/10.5194/nhess-24-631-2024, https://doi.org/10.5194/nhess-24-631-2024, 2024
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We performed field investigations on a rockfall near Jiguanshan National Forest Park, Chengdu. Vital information was obtained from an unmanned aerial vehicle survey. A finite element model was created to reproduce the damage evolution. We found that the impact kinetic energy was below the design protection energy. Improper member connections prevent the barrier from producing significant deformation to absorb energy. Damage is avoided by improving the ability of the nets and ropes to slide.
Marko Sinčić, Sanja Bernat Gazibara, Mauro Rossi, and Snježana Mihalić Arbanas
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-29, https://doi.org/10.5194/nhess-2024-29, 2024
Revised manuscript accepted for NHESS
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The paper focuses on classifying continuous landslide conditioning factors for susceptibility modelling, which resulted in 54 landslide susceptibility models that tested 11 classification criteria in combination with five statistical methods. The novelty of the research is that using stretched landslide conditioning factor values results in models with higher accuracy and that certain statistical methods are more sensitive to the landslide conditioning factor classification criteria than others.
Sudhanshu Dixit, Srikrishnan Siva Subramanian, Piyush Srivastava, Ali P. Yunus, Tapas Ranjan Martha, and Sumit Sen
Nat. Hazards Earth Syst. Sci., 24, 465–480, https://doi.org/10.5194/nhess-24-465-2024, https://doi.org/10.5194/nhess-24-465-2024, 2024
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Rainfall intensity–duration (ID) thresholds can aid in the prediction of natural hazards. Large-scale sediment disasters like landslides, debris flows, and flash floods happen frequently in the Himalayas because of their propensity for intense precipitation events. We provide a new framework that combines the Weather Research and Forecasting (WRF) model with a regionally distributed numerical model for debris flows to analyse and predict intense rainfall-induced landslides in the Himalayas.
Jacob B. Woodard, Benjamin B. Mirus, Nathan J. Wood, Kate E. Allstadt, Benjamin A. Leshchinsky, and Matthew M. Crawford
Nat. Hazards Earth Syst. Sci., 24, 1–12, https://doi.org/10.5194/nhess-24-1-2024, https://doi.org/10.5194/nhess-24-1-2024, 2024
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Dividing landscapes into hillslopes greatly improves predictions of landslide potential across landscapes, but their scaling is often arbitrarily set and can require significant computing power to delineate. Here, we present a new computer program that can efficiently divide landscapes into meaningful slope units scaled to best capture landslide processes. The results of this work will allow an improved understanding of landslide potential and can help reduce the impacts of landslides worldwide.
Anne Felsberg, Zdenko Heyvaert, Jean Poesen, Thomas Stanley, and Gabriëlle J. M. De Lannoy
Nat. Hazards Earth Syst. Sci., 23, 3805–3821, https://doi.org/10.5194/nhess-23-3805-2023, https://doi.org/10.5194/nhess-23-3805-2023, 2023
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The Probabilistic Hydrological Estimation of LandSlides (PHELS) model combines ensembles of landslide susceptibility and of hydrological predictor variables to provide daily, global ensembles of hazard for hydrologically triggered landslides. Testing different hydrological predictors showed that the combination of rainfall and soil moisture performed best, with the lowest number of missed and false alarms. The ensemble approach allowed the estimation of the associated prediction uncertainty.
Di Wu, Yuke Wang, and Xin Chen
EGUsphere, https://doi.org/10.5194/egusphere-2023-2318, https://doi.org/10.5194/egusphere-2023-2318, 2023
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This paper proposed 3D limit analysis for seismic stability of soil slopes to address the influence of earthquake on slope stabilities with nonlinear and linear criteria. Comparison results illustrated that the use of linear envelope leads to the non-negligible overestimation of steep slope stability and this overestimation will be significant with the increasing earthquake. Earthquake has a smaller influence on slope slip surface with nonlinear envelope than that with linear envelope.
Mark Bloomberg, Tim Davies, Elena Moltchanova, Tom Robinson, and David Palmer
EGUsphere, https://doi.org/10.5194/egusphere-2023-2695, https://doi.org/10.5194/egusphere-2023-2695, 2023
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Debris flows occur infrequently, with average recurrence intervals (ARIs) ranging from decades to millennia. Consequently, they pose an underappreciated hazard. We describe how to make a preliminary identification of debris flow-susceptible catchments, estimate threshold ARIs for debris flows which pose an unacceptable risk to life, and identify the "window of non-recognition" where debris flows are infrequent enough that their hazard is unrecognised, yet frequent enough to pose a risk to life.
Xushan Shi, Bo Chai, Juan Du, Wei Wang, and Bo Liu
Nat. Hazards Earth Syst. Sci., 23, 3425–3443, https://doi.org/10.5194/nhess-23-3425-2023, https://doi.org/10.5194/nhess-23-3425-2023, 2023
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A 3D stability analysis method is proposed for biased rockfall with external erosion. Four failure modes are considered according to rockfall evolution processes, including partial damage of underlying soft rock and overall failure of hard rock blocks. This method is validated with the biased rockfalls in the Sichuan Basin, China. The critical retreat ratio from low to moderate rockfall susceptibility is 0.33. This method could facilitate rockfall early identification and risk mitigation.
Marius Schneider, Nicolas Oestreicher, Thomas Ehrat, and Simon Loew
Nat. Hazards Earth Syst. Sci., 23, 3337–3354, https://doi.org/10.5194/nhess-23-3337-2023, https://doi.org/10.5194/nhess-23-3337-2023, 2023
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Rockfalls and their hazards are typically treated as statistical events based on rockfall catalogs, but only a few complete rockfall inventories are available today. Here, we present new results from a Doppler radar rockfall alarm system, which has operated since 2018 at a high frequency under all illumination and weather conditions at a site where frequent rockfall events threaten a village and road. The new data set is used to investigate rockfall triggers in an active rockslide complex.
Annette I. Patton, Lisa V. Luna, Joshua J. Roering, Aaron Jacobs, Oliver Korup, and Benjamin B. Mirus
Nat. Hazards Earth Syst. Sci., 23, 3261–3284, https://doi.org/10.5194/nhess-23-3261-2023, https://doi.org/10.5194/nhess-23-3261-2023, 2023
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Landslide warning systems often use statistical models to predict landslides based on rainfall. They are typically trained on large datasets with many landslide occurrences, but in rural areas large datasets may not exist. In this study, we evaluate which statistical model types are best suited to predicting landslides and demonstrate that even a small landslide inventory (five storms) can be used to train useful models for landslide early warning when non-landslide events are also included.
Sandra Melzner, Marco Conedera, Johannes Hübl, and Mauro Rossi
Nat. Hazards Earth Syst. Sci., 23, 3079–3093, https://doi.org/10.5194/nhess-23-3079-2023, https://doi.org/10.5194/nhess-23-3079-2023, 2023
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The estimation of the temporal frequency of the involved rockfall processes is an important part in hazard and risk assessments. Different methods can be used to collect and analyse rockfall data. From a statistical point of view, rockfall datasets are nearly always incomplete. Accurate data collection approaches and the application of statistical methods on existing rockfall data series as reported in this study should be better considered in rockfall hazard and risk assessments in the future.
Stefan Hergarten
Nat. Hazards Earth Syst. Sci., 23, 3051–3063, https://doi.org/10.5194/nhess-23-3051-2023, https://doi.org/10.5194/nhess-23-3051-2023, 2023
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Rockslides are a major hazard in mountainous regions. In formerly glaciated regions, the disposition mainly arises from oversteepened topography and decreases through time. However, little is known about this decrease and thus about the present-day hazard of huge, potentially catastrophic rockslides. This paper presents a new theoretical framework that explains the decrease in maximum rockslide size through time and predicts the present-day frequency of large rockslides for the European Alps.
Colin K. Bloom, Corinne Singeisen, Timothy Stahl, Andrew Howell, Chris Massey, and Dougal Mason
Nat. Hazards Earth Syst. Sci., 23, 2987–3013, https://doi.org/10.5194/nhess-23-2987-2023, https://doi.org/10.5194/nhess-23-2987-2023, 2023
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Landslides are often observed on coastlines following large earthquakes, but few studies have explored this occurrence. Here, statistical modelling of landslides triggered by the 2016 Kaikōura earthquake in New Zealand is used to investigate factors driving coastal earthquake-induced landslides. Geology, steep slopes, and shaking intensity are good predictors of landslides from the Kaikōura event. Steeper slopes close to the coast provide the best explanation for a high landslide density.
Yi-Min Huang
Nat. Hazards Earth Syst. Sci., 23, 2649–2662, https://doi.org/10.5194/nhess-23-2649-2023, https://doi.org/10.5194/nhess-23-2649-2023, 2023
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Debris flows are common hazards in Taiwan, and debris-flow early warning is important for disaster responses. The rainfall thresholds of debris flows are analyzed and determined in terms of rainfall intensity, accumulated rainfall, and rainfall duration, based on case histories in Taiwan. These thresholds are useful for disaster management, and the cases in Taiwan are useful for global debris-flow databases.
Davide Notti, Martina Cignetti, Danilo Godone, and Daniele Giordan
Nat. Hazards Earth Syst. Sci., 23, 2625–2648, https://doi.org/10.5194/nhess-23-2625-2023, https://doi.org/10.5194/nhess-23-2625-2023, 2023
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We developed a cost-effective and user-friendly approach to map shallow landslides using free satellite data. Our methodology involves analysing the pre- and post-event NDVI variation to semi-automatically detect areas potentially affected by shallow landslides (PLs). Additionally, we have created Google Earth Engine scripts to rapidly compute NDVI differences and time series of affected areas. Datasets and codes are stored in an open data repository for improvement by the scientific community.
Simon Seelig, Thomas Wagner, Karl Krainer, Michael Avian, Marc Olefs, Klaus Haslinger, and Gerfried Winkler
Nat. Hazards Earth Syst. Sci., 23, 2547–2568, https://doi.org/10.5194/nhess-23-2547-2023, https://doi.org/10.5194/nhess-23-2547-2023, 2023
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A rapid sequence of cascading events involving thermokarst lake outburst, rock glacier front failure, debris flow development, and river blockage hit an alpine valley in Austria during summer 2019. We analyze the environmental conditions initiating the process chain and identify the rapid evolution of a thermokarst channel network as the main driver. Our results highlight the need to account for permafrost degradation in debris flow hazard assessment studies.
Camilla Lanfranconi, Paolo Frattini, Gianluca Sala, Giuseppe Dattola, Davide Bertolo, Juanjuan Sun, and Giovanni Battista Crosta
Nat. Hazards Earth Syst. Sci., 23, 2349–2363, https://doi.org/10.5194/nhess-23-2349-2023, https://doi.org/10.5194/nhess-23-2349-2023, 2023
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This paper presents a study on rockfall dynamics and hazard, examining the impact of the presence of trees along slope and block fragmentation. We compared rockfall simulations that explicitly model the presence of trees and fragmentation with a classical approach that accounts for these phenomena in model parameters (both the hazard and the kinetic energy change). We also used a non-parametric probabilistic rockfall hazard analysis method for hazard mapping.
Ascanio Rosi, William Frodella, Nicola Nocentini, Francesco Caleca, Hans Balder Havenith, Alexander Strom, Mirzo Saidov, Gany Amirgalievich Bimurzaev, and Veronica Tofani
Nat. Hazards Earth Syst. Sci., 23, 2229–2250, https://doi.org/10.5194/nhess-23-2229-2023, https://doi.org/10.5194/nhess-23-2229-2023, 2023
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This work was carried out within the Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia (SFRARR) project and is focused on the first landslide susceptibility analysis at a regional scale for Central Asia. The most detailed available landslide inventories were implemented in a random forest model. The final aim was to provide a useful tool for reduction strategies to landslide scientists, practitioners, and administrators.
Francis K. Rengers, Luke A. McGuire, Katherine R. Barnhart, Ann M. Youberg, Daniel Cadol, Alexander N. Gorr, Olivia J. Hoch, Rebecca Beers, and Jason W. Kean
Nat. Hazards Earth Syst. Sci., 23, 2075–2088, https://doi.org/10.5194/nhess-23-2075-2023, https://doi.org/10.5194/nhess-23-2075-2023, 2023
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Debris flows often occur after wildfires. These debris flows move water, sediment, and wood. The wood can get stuck in channels, creating a dam that holds boulders, cobbles, sand, and muddy material. We investigated how the channel width and wood length influenced how much sediment is stored. We also used a series of equations to back calculate the debris flow speed using the breaking threshold of wood. These data will help improve models and provide insight into future field investigations.
Maxime Morel, Guillaume Piton, Damien Kuss, Guillaume Evin, and Caroline Le Bouteiller
Nat. Hazards Earth Syst. Sci., 23, 1769–1787, https://doi.org/10.5194/nhess-23-1769-2023, https://doi.org/10.5194/nhess-23-1769-2023, 2023
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In mountain catchments, damage during floods is generally primarily driven by the supply of a massive amount of sediment. Predicting how much sediment can be delivered by frequent and infrequent events is thus important in hazard studies. This paper uses data gathered during the maintenance operation of about 100 debris retention basins to build simple equations aiming at predicting sediment supply from simple parameters describing the upstream catchment.
Elsa S. Culler, Ben Livneh, Balaji Rajagopalan, and Kristy F. Tiampo
Nat. Hazards Earth Syst. Sci., 23, 1631–1652, https://doi.org/10.5194/nhess-23-1631-2023, https://doi.org/10.5194/nhess-23-1631-2023, 2023
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Landslides have often been observed in the aftermath of wildfires. This study explores regional patterns in the rainfall that caused landslides both after fires and in unburned locations. In general, landslides that occur after fires are triggered by less rainfall, confirming that fire helps to set the stage for landslides. However, there are regional differences in the ways in which fire impacts landslides, such as the size and direction of shifts in the seasonality of landslides after fires.
Stefan Steger, Mateo Moreno, Alice Crespi, Peter James Zellner, Stefano Luigi Gariano, Maria Teresa Brunetti, Massimo Melillo, Silvia Peruccacci, Francesco Marra, Robin Kohrs, Jason Goetz, Volkmar Mair, and Massimiliano Pittore
Nat. Hazards Earth Syst. Sci., 23, 1483–1506, https://doi.org/10.5194/nhess-23-1483-2023, https://doi.org/10.5194/nhess-23-1483-2023, 2023
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We present a novel data-driven modelling approach to determine season-specific critical precipitation conditions for landslide occurrence. It is shown that the amount of precipitation required to trigger a landslide in South Tyrol varies from season to season. In summer, a higher amount of preparatory precipitation is required to trigger a landslide, probably due to denser vegetation and higher temperatures. We derive dynamic thresholds that directly relate to hit rates and false-alarm rates.
Yaspal Sundriyal, Vipin Kumar, Neha Chauhan, Sameeksha Kaushik, Rahul Ranjan, and Mohit Kumar Punia
Nat. Hazards Earth Syst. Sci., 23, 1425–1431, https://doi.org/10.5194/nhess-23-1425-2023, https://doi.org/10.5194/nhess-23-1425-2023, 2023
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The NW Himalaya has been one of the most affected terrains of the Himalaya, subject to disastrous landslides. This article focuses on two towns (Joshimath and Bhatwari) of the NW Himalaya, which have been witnessing subsidence for decades. We used a slope stability simulation to determine the response of the hillslopes accommodating these towns under various loading conditions. We found that the maximum displacement in these hillslopes might reach up to 20–25 m.
Cited articles
Avouac, J.-P., Meng, L., Wei, S., Wang, T., and Ampuero, J.-P.: Lower edge of locked Main Himalayan Thrust unzipped by the 2015 Gorkha earthquake, Nat. Geosci., 8, 708–711, https://doi.org/10.1038/ngeo2518, 2015.
Barrington, L., Ghosh, S., Greene, M., Har-Noy, S., Berger, J., Gill, S., Lin, A., and Huyck, C.: Crowdsourcing earthquake damage assessment using remote sensing imagery, Ann. Geophys., 54, 6, https://doi.org/10.4401/ag-5324, 2012.
Bird, J. and Bommer, J.: Earthquake losses due to ground failure, Eng. Geol., 75, 147–179, https://doi.org/10.1016/j.enggeo.2004.05.006, 2004.
Budimir, M. E. A., Atkinson, P. M., and Lewis, H. G.: Earthquake-and-landslide events are associated with more fatalities than earthquakes alone, Nat. Hazards, 72, 895–914, https://doi.org/10.1007/s11069-014-1044-4, 2014.
Casagli, N., Cigna, F., Bianchini, S., Hölbling, D., Füreder, P., Righini, G., Del Conte, S., Friedl, B., Schneiderbauer, S., Iasio, C., Vlcko, J, Greif, V., Proske, H., Granica, K., Falco, S., Lozzi, S., Mora, O., Arnaud, A., Novali, F., and Bianchi, M.: Landslide mapping and monitoring by using radar and optical remote sensing: examples from the EC-FP7 project SAFER, P-Soc. Photo-Soc. Env., 4, 92–108, https://doi.org/10.1016/j.rsase.2016.07.001, 2016.
Collins, B. and Jibson, R.: Assessment of existing and potential landslide hazards resulting from the April 25, 2015 Gorkha, Nepal earthquake sequence, US Geological Survey Open File Report No. 2015-1142, US Geological Survey, Reston, VA, USA, https://doi.org/10.3133/ofr20151142, 2015.
Costa, J. E. and Schuster, R. L.: The formation and failure of natural dams, Geol. Soc. Am. Bull., 100, 1056–1068 1987.
Cui, P., Zhu, Y., Han, Y., Chen, X., and Zhuang, J.: The 12 May Wenchuan earthquake-induced landslide lakes: distribution and preliminary risk evaluation, Landslides, 6, 209–223, https://doi.org/10.1007/s10346-009-0160-9, 2009.
Datta, A., Sigdel, S., Oven, K. J., Rosser, N. J., Densmore, A. L., and Rijal, S.: The role of scientific evidence during the 2015 Nepal earthquake relief effort, ODI Report, Overseas Development Institute, London, in press, 2018.
Davies, T., Beaven, S., Conradson, D., Densmore, A., Gaillard, J. C., Johnston, D., Milledge, D., Oven, K., Petley, D., Rigg, J., and Robinson, T.: Towards disaster resilience: A scenario-based approach to co-producing and integrating hazard and risk knowledge, Int. J. Disaster Risk Res., 13, 242–247, 2015.
Duda, K. A. and Jones, B. K.: USGS remote sensing coordination for the 2010 Haiti earthquake, P. Eng. Remote Sensing, 77, 899–907, https://doi.org/10.14358/PERS.77.9.899, 2011.
Đurić, D., Mladenović, A., Pešić-Georgiadis, M., Marjanović, M., and Abolmasov, B.: Using multiresolution and multitemporal satellite data for post-disaster landslide inventory in the Republic of Serbia, Landslides, 14, 1467–1482, https://doi.org/10.1007/s10346-017-0847-2, 2017.
ECHO: Crisis Falsh No. 10, European Commision Humanitarian Aid and Civil Protection Report, available at: http://www.europarl.europa.eu/meetdocs/2014_2019/documents/deve/dv/echo_crisis_flash_10_nepal_earthquake_/echo_crisis_flash_10_nepal_earthquake_en.pdf (last access: 1 October 2016), 2015.
Fleischhauer, S., Behr, F.-J., and Rawiel, P.: Concept and implementation of an architecture for the immediate provision of geodata in disaster management, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W2, 73–78, https://doi.org/10.5194/isprs-archives-XLII-4-W2-73-2017, 2017.
Galetzka, J., Melgar, D., Genrich, J. F., Geng, J., Owen, S., Lindsey, E. O., Xu, X., Bock, Y., Avouac, J. P., Adhikari, L. B., and Upreti, B. N.: Slip pulse and resonance of the Kathmandu basin during the 2015 Gorkha earthquake, Nepal, Science, 349, 1091–1095, https://doi.org/10.1126/science.aac6383, 2015.
Gallen, S. F., Clark, M. K., Godt, J. W., Roback, K., and Niemi, N. A.: Application and evaluation of a rapid response earthquake-triggered landslide model to the 25 April 2015 Mw 7.8 Gorkha earthquake, Nepal, Tectonophysics, 714–715, 173–187, https://doi.org/10.1016/j.tecto.2016.10.031, 2016.
Goodchild, M. F.: Recognition of GIS critical, GIM International, 21, 431, 2007.
Goodchild, M. F. and Glennon, A.: Crowdsourcing geographic information for disaster response: a research frontier, Int. J. Digit. Earth, 3, 231–241, https://doi.org/10.1080/17538941003759255, 2010.
Gorum, T., Fan, X., van Westen, C., Huang, R., Xu, Q., Tang, C., and Wang, G.: Distribution pattern of earthquake-induced landslides triggered by the 12 May 2008 Wenchuan earthquake, Geomorphology, 133, 152–167, https://doi.org/10.1016/j.geomorph.2010.12.030, 2011.
Guzzetti, F.: Landslide hazard and risk assessment, PhD Thesis, University of Bonn, Bonn, Germany, 2004.
Guzzetti, F., Mondini, A. C., Cardinali, M., and Fiorucci, F.: Landslide inventory maps: New tools for an old problem, Earth Sci. Rev., 112, 42–66, https://doi.org/10.1080/17445647.2014.949313, 2012.
Harp, E., Keefer, D., Sato, H., and Yagi, H.: Landslide inventories: The essential part of seismic landslide hazard analyses, Eng. Geol., 122, 9–21, https://doi.org/10.1016/j.enggeo.2010.06.013, 2011.
Hölbling, D., Eisank, C., Albrecht, F., Vecchiotti, F., Friedl, B., Weinke, E., and Kociu, A.: Comparing Manual and Semi-Automated Landslide Mapping Based on Optical Satellite Images from Different Sensors, Geosciences, 7, 37–56, https://doi.org/10.3390/geosciences7020037, 2017.
Houbourg, R. and McCabe, M. F.: High-Resolution NDVI from Planet's Constellation of Earth Observing Nano-Satellites: A New Data Source for Precision Agriculture, Remote Sens., 8, 786–804, https://doi.org/10.3390/rs8090768, 2016.
IASC: Multi-sector initial rapid assessment guidance (MIRA), Inter-Agency Standing Committee, available at: https://docs.unocha.org/sites/dms/documents/mira_final_version2012.pdf (last access: 1 March 2017), 2012.
IASC: Multi-sector initial rapid assessment guidance, Inter-Agency Standing Committee, available at: https://www.humanitarianresponse.info/en/programme-cycle/space/document/multi-sector-initial-rapid-assessment-guidance-revision-july-2015 (last access: 1 March 2017), 2015.
Jin, Y. Q. and Wang, D.: Automatic Detection of Terrain Surface Changes after Wenchuan Earthquake, May 2008, from ALOS SAR Images Using 2EM-MRF Method, IEEE Geosci. Remote Sens. Lett., 6, 344–348, https://doi.org/10.1109/LGRS.2009.2013875, 2009.
Joyce, K., Belliss, S., Samsonov, S., McNeill, S., and Glassey, P.: A review of the status of satellite remote sensing and image processing techniques for mapping natural hazards and disasters, Prog. Phys. Geoge., 33, 183–207, https://doi.org/10.1177/0309133309339563, 2009.
Kargel, J. S., Leonard, G. J., Shugar, D. H., Haritashya, U. K., Bevington, A., Fielding, E. J., Fujita, K., Geertsema, M., Miles, E. S., Steiner, J., Anderson, E., Bajracharya,S., Bawden G. W., Breashears, D. F., Byers, A., Collins, B., Dhital, M. R., Donnellan, A., Evans, T. L., Geai, M. L., Glasscoe, M. T., Green, D., Gurung, D. R., Heijenk, R., Hilborn, A., Hudnut, K., Huyck, C., Immerzeel, W. W., Liming, J., Jibson, R., Kääb, A., Khanal, N. R., Kirschbaum, D., Kraaijenbrink, P. D. A., Lamsal, D., Shiyin, L., Mingyang, L., McKinney, D., Nahirnick, N. K., Zhuotong, N., Ojha, S., Olsenholler, J., Painter, T. H., Pleasants, M., Pratima, K. C., Yuan, Q. I., Raup, B. H., Regmi, D., Rounce, D. R., Sakai, A., Donghui, S., Shea, J. M., Shrestha, A. B., Shukla, A., Stumm, D., van der Kooij, M., Voss, K., Xin, W., Weihs, B., Wolfe, D., Lizong, W., Xiaojun, Y., Yoder, M. R., and Young, N.: Geomorphic and geologic controls of geohazards induced by Nepal's 2015 Gorkha earthquake, Science, 351, aac8353, https://doi.org/10.1126/science.aac8353, 2016.
Keefer, D. K.: Landslides caused by earthquakes, Geol. Soc. Am. Bull., 95, 406–421, https://doi.org/10.1130/0016-7606(1984)95<406:LCBE>2.0.CO;2, 1984.
Kirschbaum, D. B., Adler, R., Hong, Y., Hill, S., and Lerner-Lam, A.: A global landslide catalog for hazard applications: method, results, and limitations, Nat. Hazards, 52, 561–575, https://doi.org/10.1007/s11069-009-9401-4, 2010.
Kritikos, T., Robinson, T. R., and Davies, T. R.: Regional coseismic landslide hazard assessment without historical landslide inventories: A new approach, J. Geophys. Res.-Ea. Surf., 120, 711–729, https://doi.org/10.1002/2014JF003224, 2015.
Lu, P., Stumpf, A., Kerle, N., and Casagli, N.: Object-oriented change detection for landslide rapid mapping, IEEE Geosci. Remote. Sens. Lett., 8, 701–705, https://doi.org/10.1109/LGRS.2010.2101045, 2011.
Mahmood, I., Kidwai, A., Qureshi, S., Iqbal, M., and Atique, L.: Revisiting major earthquakes in Pakistan, Geology Today, 31, 33–38, https://doi.org/10.1111/gto.12085, 2015.
Marc, O. and Hovius, N.: Amalgamation in landslide maps: effects and automatic detection, Nat. Hazards Earth Syst. Sci., 15, 723–733, https://doi.org/10.5194/nhess-15-723-2015, 2015.
Marc, O., Hovius, N., Meunier, P., Uchida, T., and Hayashi, S.: Transient changes of landslide rates after earthquakes, Geology, 43, 883–886, https://doi.org/10.1130/G36961.1, 2015.
Marc, O., Hovius, N., Meunier, P., Gorum, T., and Uchida, T.: A seismologically consistent expression for the total area and volume of earthquake-triggered landsliding, J. Geophys. Res.-Ea. Surf., 121, 640–663, https://doi.org/10.1002/2015JF003732, 2016.
Marc, O., Meunier, P., and Hovius, N.: Prediction of the area affected by earthquake-induced landsliding based on seismological parameters, Nat. Hazards Earth Syst. Sci., 17, 1159–1175, https://doi.org/10.5194/nhess-17-1159-2017, 2017.
Martha, T. R., Kerle, N., Jetten, V., van Westen, C. J., and Kumar, K. V.: Characterising spectral, spatial and morphometric properties of landslides for semi-automatic detection using object-oriented methods, Geomorphology, 116, 24–36, https://doi.org/10.1016/j.geomorph.2009.10.004, 2010.
Martha, T. R., Roy, P., Mazumdar, R., Govindharaj, K. B., and Kumar, K. V.: Spatial characteristics of landslides triggered by the 2015 Mw 7.8 (Gorkha) and Mw 7.3 (Dolakha) earthquakes in Nepal, Landslides, 14, 697–704, https://doi.org/10.1007/s10346-016-0763-x, 2016.
Meunier, P., Hovius, N., and Haines, J.: Topographic site effects and the location of earthquake induced landslides, Earth Planet Sc. Lett., 275, 221–232, https://doi.org/10.1016/j.epsl.2008.07.020, 2008.
Moine, M., Puissant, A., and Malet, J.-P.: Detection of landslides from aerial and satellite images with a semi-automatic method. Application to the Barcelonnette basin (Alpes-de-Hautes-Provence, France), in: Landslide processes: From geomorphologic mapping to dynamic modelling, 6–7 February 2009, Strasbourg, France, 63–68, 2009.
Mondini, A. C.: Measures of Spatial Autocorrelation Changes in Multitemporal SAR images for event landslides detection, Remote Sensing, 9, 554–568, https://doi.org/10.3390/rs9060554, 2017.
Mondini, A. C., Guzzetti, F., Reichenbach, P., Rossi, M., Cardinali, M., and Ardizzone, F.: Semi-automatic recognition and mapping of rainfall induced shallow landslides using optical satellite images, Remote Sens. Environ., 115, 1743–1757, https://doi.org/10.1016/j.rse.2011.03.006, 2011.
Nepalese Army: The Nepalese Army in the Afternamth of the Gorkha Earthquake of 2015 – Experiences and lessons learned, Publication of the Nepalese Army, Kathmandu, 52 pp., 2015.
OCHA: United Nations Disaster Assessment and Coordination, United Nations Office for the Coordination of Humanitarian Affairs, available at: https://www.unocha.org/sites/unocha/files/dms/Documents/UNDAC Handbook 2013_english_final.pdf (last access: 15 January 2018), 2013.
Owen, L., Kamp, U., Khattak, G., Harp, E., Keefer, D., and Bauer, M.: Landslides triggered by the 8 October 2005 Kashmir earthquake, Geomorphology, 94, 1–9, https://doi.org/10.1016/j.geomorph.2007.04.007, 2008.
Parker, R. N., Hancox, G. T., Petley, D. N., Massey, C. I., Densmore, A. L., and Rosser, N. J.: Spatial distributions of earthquake-induced landslides and hillslope preconditioning in the northwest South Island, New Zealand, Earth Surf. Dynam., 3, 501–525, https://doi.org/10.5194/esurf-3-501-2015, 2015.
Parker, R. N., Rosser, N. J., and Hales, T. C.: Spatial prediction of earthquake-induced landslide probability, Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2017-193, in review, 2017.
Pellicani, R., Westen, C., and Spilotro, G.: Assessing landslide exposure in areas with limited landslide information, Landslides, 11, 463–480, https://doi.org/10.1007/s10346-013-0386-4, 2014.
Petley, D.: Global patterns of loss of life from landslides, Geology, 40, 927–930, https://doi.org/10.1130/G33217.1, 2012.
Petley, D., Dunning, S., Rosser, N., and Kausar, A.: Incipient landslides in the Jhelum Valley, Pakistan following the 8th October 2005 earthquake, in: Disaster Mitigation of Debris Flows, Slope Failures and Landslides, Frontiers of Sci. Ser., University Academy Press, Tokyo, Japan, 47–56, 2006.
Petley, D., Hearn, G., Hart, A., Rosser, N., Dunning, S., Oven, K., and Mitchell, W.: Trends in landslide occurrence in Nepal, Nat. Hazards, 43, 23–44, https://doi.org/10.1007/s11069-006-9100-3, 2007.
Plank, S., Twele, A., and Martinis, S.: Landslide mapping in vegetated areas using change detection based on optical and polarimetric SAR data, Remote Sensing, 8, 307–326, https://doi.org/10.3390/rs8040307, 2016.
Poiani, T., dos Rocha, S. R., and Degrossi, L. C.: Potential of collaborative mapping for disaster relief: A case study of OpenStreetMap in the Nepal earthquake 2015, in: 49th Hawaii International Conference on System Sciences, 6–7 January 2016, Hawaii, 188–197, 2016.
Raspini, F., Ciampalini, A., Del Conte, S., Lombardi, L., Nocentini, M., Gigli, G., Ferretti, A., and Casagli, N.: Exploitation of amplitude and phase of satellite SAR images for landslide mapping: the case of Montescaglioso (South Italy), Remote Sensing, 7, 14576–14596, https://doi.org/10.3390/rs71114576, 2015.
Roback, K., Clark, M. K., West, A. J., Zekkos, D., and Li, G.: The size, distribution, and mobility of landslides caused by the 2015 Mw 7.8 Gorkha earthquake, Nepal, Geomorphology, 301, 121–138, https://doi.org/10.1016/j.geomorph.2017.01.030, 2017.
Robinson, T., Davies, T., Wilson, T., Orchiston, C., and Barth, N.: Evaluation of coseismic landslide hazard on the proposed Haast-Hollyford Highway, South Island, New Zealand, Georisk, 10, 146–163, https://doi.org/10.1080/17499518.2015.1077974, 2015.
Robinson, T. R., Rosser, N. J., Densmore, A. L., Williams, J. G., Kincey, M. E., Benjamin, J., and Bell, H. J. A.: Rapid post-earthquake modelling of coseismic landslide intensity and distribution for emergency response decision support, Nat. Hazards Earth Syst. Sci., 17, 1521–1540, https://doi.org/10.5194/nhess-17-1521-2017, 2017.
Roche, S., Propeck-Zimmermann, E., and Mericskay, B.: GeoWeb and crisis management: Issues and perspectives of volunteered geographic information, GeoJournal, 78, 21–40, https://doi.org/10.1007/s10708-011-9423-9, 2013.
Sato, H. P. and Harp, E. L.: Interpretation of earthquake-induced landslides triggered by the 12 May 2008, M7.9 Wenchuan earthquake in the Beichuan area, Sichuan Province, China using satellite imagery and Google Earth, Landslides, 6, 153–159, https://doi.org/10.1007/s10346-009-0147-6, 2009.
Sortiris, V., George, P., and Spyros, P.: Map of Co-Seismic Landslides and Surface Ruptures for the M 7.8 Kaikoura, New Zealand Earthquake – Version 2, available at: http://eqgeogr.weebly.com/blog/map-of-co-seismic-landslides-surface-ruptures-for-the-m-78, last access: 15 January 2018.
Stumpf, A. and Kerle, N.: Object-oriented mapping of landslides using Random Forests, Remote Sens. Environ., 115, 2564–2577, https://doi.org/10.1016/j.rse.2011.05.013, 2011.
Stumpf, A., Marc, O., Malet, J. P., and Michea, D.: Sentinel-2 for rapid operational landslide inventory mapping, 23–28 April 2017, EGU General Assembly Conference, 19, 4449, 2017.
Tiwari, B., Ajmera, B., and Dhital, S.: Characteristics of moderate-to large-scale landslides triggered by the Mw 7.8 2015 Gorkha earthquake and its aftershocks, Landslides, 14, 1297–1318, https://doi.org/10.1007/s10346-016-0789-0, 2017.
Tsai, F., Hwang, J.-H., Chen, L.-C., and Lin, T.-H.: Post-disaster assessment of landslides in southern Taiwan after 2009 Typhoon Morakot using remote sensing and spatial analysis, Nat. Hazards Earth Syst. Sci., 10, 2179–2190, https://doi.org/10.5194/nhess-10-2179-2010, 2010.
UN-SPIDER: Emergency mapping guidelines, International Working Group on Satellite-based Emergency Mapping (IWG-SEM), http://www.un-spider.org/network/iwg-sem, last access: 13 November 2017.
Voigt, S., Giulio-Tonolo, F., Lyons, J., Kučera, J., Jones, B., Schneiderhan, T., Platzeck, G., Kaku, K., Hazarika, M. K., Czaran, L., and Li, S.: Global trends in satellite-based emergency mapping, Science, 353, 247–252, https://doi.org/10.1126/science.aad8728, 2016.
Wasowski, J., Keefer, D., and Lee, C.-T.: Toward the next generation of research on earthquake-induced landslides: Current issues and future challenges, Eng. Geol., 122, 1–8, https://doi.org/10.1016/j.enggeo.2011.06.001, 2011.
Xu, C.: Preparation of earthquake-triggered landslide inventory maps using remote sensing and GIS technologies: Principles and case studies, Geosci. Front., 6, 825–836, https://doi.org/10.1016/j.gsf.2014.03.004, 2015.
Xu, C., Xu, X. W., Yao, X., and Dai, F. C.: Three (nearly) complete inventories of landslides triggered by the May 12, 2008 Wenchuan Mw 7.9 earthquake of China and their spatial distribution statistical analysis, Landslides, 11, 441–461, https://doi.org/10.1007/s10346-013-0404-6, 2014.
Yin, Y., Wang, F., and Sun, P.: Landslide hazards triggered by the 2008 Wenchuan earthquake, Sichuan, China, Landslides, 6, 139–152, https://doi.org/10.1007/s10346-009-0148-5, 2009.
Zook, M., Graham, M., and Shelton, T.: Volunteered geographic information and crowdsourcing disaster relief: a case study of the Haitian earthquake, World Med. Health Policy, 2, 7–33, https://doi.org/10.2202/1948-4682.1069, 2010.
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Short summary
There is currently no protocol for rapid humanitarian-facing landslide assessment and no published recognition of what is possible and useful to compile immediately after a triggering event. Drawing on the 2015 Gorkha earthquake (Nepal), we consider how quickly a landslide assessment based upon manual satellite-based emergency mapping (SEM) can be realistically achieved and review the decisions taken by analysts to ascertain the timeliness and type of useful information that can be generated.
There is currently no protocol for rapid humanitarian-facing landslide assessment and no...
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